Audience matching network with performance factoring and revenue allocation

ABSTRACT

Performance based delivery of content to an audience member. A network of audience member information collection domains provide information about audience members. A profiled audience member may belong to a network segment. Delivery of an advertisement to the profiled audience member is accommodated based upon membership in the network segment and performance criteria. The performance criteria may be configured to benefit a publisher, such as by maximizing revenue. Allocating revenue based upon the collection of data used to target audience members is also provided.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 10/981,733 filed on Nov. 5, 2004, and entitled “AudienceTargeting System with Segment Management,” which is acontinuation-in-part of U.S. patent application Ser. No. 10/669,791,filed on Sep. 25, 2003, and entitled “System and Method for Segmentingand Targeting Audience Members,” which claims the benefit under 35 USC §119 of Provisional Patent Application No. 60/491,521, filed on Aug. 1,2003. The entire contents of these Applications are hereby incorporatedby reference.

BACKGROUND OF THE INVENTION 1. Field of the Invention

This invention relates generally to audience targeting and moreparticularly to matching an audience with deliverable content such asadvertising.

2. Description of the Related Art

Targeted marketing has long been known as an effective method forreaching consumers. When the consumer receives only relevant content(advertisements, etc.) from a provider, the consumer is more likely topatronize the particular provider, make purchases, and provideadditional personal information that may assist in refining theprovider's “view” of the consumer. As such, targeted marketing can leadto a more focused and robust interaction with the consumer. This,correspondingly, can lead to a more rewarding interaction for theprovider by generating increased revenue.

In order to effectively target a consumer, it may be desirable formarketing systems to react to consumer information received from avariety of online and offline sources. These sources may includedatabases and servers, as well as multiple web properties within anetwork of affiliated websites. Moreover, the consumer information maybe collected from a variety of sources in diverse formats. It may alsobe desirable for marketing systems to interact with the systems thatactually deliver the content to the user. In short, an effectivemarketing system may appreciate the characteristics and preferences of aspecific user regardless of the number or type of channels through whichcontact with the user is made.

Some known systems, however, are only adapted to receive informationfrom a single source (e.g., registration information provided by theconsumer). Other systems may receive information from multiple sources,but are unable to usefully combine information relating to the sameconsumer and communicate it to the necessary content delivery system.Thus, it may be desirable to have a system and method for deliveringcontent that integrates with and aggregates data from various sources,including the underlying systems that deliver content to the consumer.

Known systems for delivering targeted content to consumers are focusedon reaching the greatest quantity of consumers, without considering thevalue of interacting with each particular consumer. For example, somesystems may deliver “targeted” content to each member of a group ofconsumers based on the fact that each subscribes to the same magazine.These systems, however, do not consider that only a portion of the groupmay make on-line purchases, for example, in addition to subscribing tothe magazine. This failure to recognize and differentiate “valuable”consumers can lead to lost revenue for the content provider. Inaddition, the delivery of content to a significant volume of low-valueconsumers may expend valuable system resources. Accordingly, it may bedesirable to have a means of delivering the appropriate content to theappropriate user in order to maximize the value of the relationshipbetween the provider and the consumer.

Another problem with content delivery systems is that the informationupon which targeting is based may rapidly become stale. An audiencemember deemed to have particular characteristics may no longer have suchcharacteristics by the time content is delivered. New potential audiencemembers may also become available after determination of a targetedgroup. The volatility of audience member characteristics and the highvolume of information to be processed both present difficulties tosystems that seek to target well tailored audiences. Content deliverysystems are also often myopic, merely carrying out content delivery asdictated by the particular domain in which the system resides. Thisprevents appreciation of activities in other domains.

Still another problem with content delivery systems, particularly thosethat seek to collect information and deliver content to particularaudience members over the Internet, is the potential for faultyidentification of audience members. For example, some systems may usecookies to attempt to uniquely identify an audience member. Thispresents potential problems because a given person may use severalcomputers and thereby generate several cookies. Software and browsermaintenance activities may also prompt the deletion of cookies. Thesevarious factors may prompt the proliferation of unnecessary andsometimes erroneous profiles.

It is also difficult for publishers to serve advertisements such thatrevenue is maximized, or accommodate proper serving of advertisements bythird party providers. Finally, the allocation of credit andcorresponding revenue for activities related to the serving ofadvertisements remains inadequate.

SUMMARY OF THE INVENTION

The present invention accommodates the delivery of content such asadvertisements to audience members.

In one aspect, performance based delivery of content to an audiencemember comprises managing audience segments corresponding to a networkof audience member information collection domains, determining that aprofiled audience member is a member of a network segment that includesaudience members having a set of attributes corresponding to profiledata collected in the network of audience member information collectiondomains, and accommodating the delivery of an advertisement to theprofiled audience member based upon membership in the network segmentand performance criteria.

The performance criteria may be configured to benefit a publisher, andmay further seek to maximize revenue to the publisher. For example,accommodating the delivery of an advertisement may involve selecting anadvertisement with the highest available payment to the publisher.

Placement of advertisements may further entail receiving from thepublisher a set of delivery criteria corresponding to potentiallydeliverable advertisements, and selecting an advertisement with thehighest available payment to the publisher based upon a comparison ofcurrent delivery metrics to the set of delivery criteria.

Another aspect optimizes the delivery of advertisement inventory servedby a third party. Optimizing the delivery of advertisement inventoryserved by the third party may include providing information aboutmembership in the network segment in connection with an advertisementserving function of the third party.

Still another aspect of the present invention provides network segmentdefinition through examination of the data profiles for audience membersthat have engaged in a desired behavior, such as responding to anadvertisement.

Still another aspect of the present invention supports allocation ofrevenue corresponding to the delivery of content to an audience member,and may comprise managing audience segments corresponding to a networkof audience member information collection domains, determining that aprofiled audience member is a member of a network segment that includesaudience members having a set of attributes corresponding to profiledata collected in the network of audience member information collectiondomains, accommodating the delivery of an advertisement to the profiledaudience member based upon membership in the network segment, andallocating revenue corresponding to the delivery of the advertisementbased upon participation as a data provider in the collection ofinformation.

The present invention can be embodied in various forms, includingbusiness processes, computer implemented methods, computer programproducts, computer systems and networks, user interfaces, applicationprogramming interfaces, and the like.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other more detailed and specific features of the presentinvention are more fully disclosed in the following specification,reference being had to the accompanying drawings, in which:

FIG. 1 is a block diagram illustrating an example of a system fordelivering content to an audience member.

FIG. 2 is a flow diagram illustrating an example of delivering contentto an audience member.

FIG. 3 is a flow diagram illustrating an example of delivering contentto an audience member.

FIG. 4 is a flow diagram illustrating an example of tracking websitepages visited by an audience member using a unique identifier.

FIG. 5 is a flow diagram illustrating an example of grouping audiencemembers into segments for receipt of targeted content.

FIG. 6 is a flow diagram illustrating an example of directing targetedcontent to audience members in a segment.

FIG. 7 is a block diagram illustrating another example of a system fordelivering content to an audience member.

FIG. 8 is a block diagram illustrating an example of an audiencetargeting system that includes segment management.

FIGS. 9A-B are respectively a block diagram illustrating an example of aparticular extractor 900 and a schematic diagram that exemplifies amodel for extracting profile data.

FIGS. 10A-B are schematic diagrams illustrating an example of a segmentmanagement architecture and corresponding calculation of segments.

FIGS. 11A-B are schematic diagram illustrating an example of processingdata tables to manage and produce segments.

FIG. 12 is a block diagram illustrating an example of an audiencetargeting system that includes profile synchronization.

FIG. 13 is a flow diagram illustrating an example of a process forprofile synchronization.

FIGS. 14A-B are schematic diagrams illustrating an example of a networkfor matching an audience with deliverable content according to thepresent invention.

FIG. 15 is a block diagram illustrating an example of an audiencematching network system according to the present invention.

FIGS. 16A, 16A-1, 16A-2, 16A-3, 16B, 16B-1, 16B-2, and 16B-3 are eventdiagrams illustrating an example of a computer implemented process formatching audience members to deliverable content according to thepresent invention.

FIG. 17 is a block diagram illustrating an example of an audiencematching network system that includes advertising revenue andperformance management according to another aspect of the presentinvention.

FIG. 18 is a flow chart illustrating an example of deliveringadvertisements to with revenue and performance management according tothis aspect of the present invention.

FIG. 19 is a block diagram illustrating an example of an audiencematching network system that includes universal profile synchronization(UPS) according to another aspect of the present invention.

FIG. 20 is a flow diagram illustrating an example of a process for UPS.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, for purposes of explanation, numerousdetails are set forth, such as flowcharts and system configurations, inorder to provide an understanding of one or more embodiments of thepresent invention. However, it is and will be apparent to one skilled inthe art that these specific details are not required in order topractice the present invention.

One embodiment of the system 10 for delivering content to an audiencemember is shown in FIG. 1. The system 10 includes a first server 120which hosts an extractor program 122. The first server 120 isoperatively connected to one or more offline databases 110, and one ormore external content servers 160. The offline databases 110 andexternal content servers 160 are also operatively connected to one ormore web servers 170. The web servers 170 may provide website pages toan audience member computer 180 in a conventional manner. The webservers 170 are also operatively connected to a targeting engine program152 resident on a second server 150. The first and second servers 120and 150 may be operatively connected to a third server 130 whichcontains a database 132 (referred to as the data warehouse) for storingaudience member profile data. In some embodiments of the presentinvention, the same server may act as the first, second, and/or thirdservers 120, 150, and 130. A control console 140 may be operativelyconnected to the third server 130.

FIG. 2 is a flow diagram illustrating an example of delivering contentto an audience member. This may include three primary stages: datacollection and profile generation; audience segmentation and analysis;and interface to external systems. During data collection and profilegeneration, offline data sources 110 are searched to collect profiledata relating to individuals (referred to as audience members). Thisprofile data is stored in the data warehouse 132. During audiencesegmentation and analysis, the profile data for audience members is usedto categorize the audience members into segments. For example, profiledata may indicate that a particular audience member subscribes to GolfMagazine, and thus has some interest in golf. That audience member maythen be included in a segment (i.e., group) of audience members that arealso interested in golf. During the interface to external systems stage,a targeting engine 152 may use the inclusion of the audience member in asegment to direct targeted external content to the audience members inthat segment. Continuing with the example posed above, audience membersin the “golf” segment may have golf related content sent to them as aresult.

With continued reference to FIG. 2, data collection and profilegeneration may involve the offline databases 110, the extractor program122, and the data warehouse 132. Initial profile information aboutindividual audience members may be collected from available databases,such as a registration database 112 and a subscription database 114 bythe extractor 122. Registration and subscription databases 112 and 114may include audience member profile data collected as a result of theaudience member registering with, or subscribing to, any type ofservice, including but not limited to an Internet, magazine, newspaper,newsletter, cable, telephone, or wireless service, for example. Theseregistration and subscription databases may include a wide variety ofprofile information such as name, gender, age, birth date, home and workaddresses, telephone numbers, credit and charge card information,marital status, income level, number and age of children, employmenthistory, hobbies, recent purchases, educational status, interests,preferences, and the like, for example.

The extractor 122 is a program that is used to parse and identifyaudience member profile data from within a set of data. The extractor122 may be constructed using Java, Perl, C++, C#, SQL, or any othersimilar programming language. The extractor 122 may be resident on aserver 120, or multiple servers. The extractor 122 may be governed by aset of extraction rules that determine the source(s) and format(s) ofprofile data that may be used to create a profile for an audiencemember, and the categories of profile data to be collected. Theextraction rules may include a series of text rules (using matchingutilities such as string matching or regular expressions) that are usedto transform data in one form into a more standardized form whileremoving unneeded data. The extraction rules may include, for example, astatement such as “if string contains ‘A’ then output result ‘B’.”

The extractor 122 is operatively connected to a database 132 referred toas the data warehouse 132. The data warehouse 132 may be provided on asecond server 130, and may be used to store the profile and segmentaffinity data relating to audience members. The extractor 122 mayroutinely update the profile and segment affinity data in the datawarehouse 132. As new or modified profile data becomes available fromthe offline databases 110, the extractor 122 may modify the profile datafor an audience member. The extractor 122 may also receive profile datadirectly from the audience member computer 180 and/or the targetingengine 152 that indicates the website pages visited, the web searchesconducted, and the emails received by the audience member.

FIG. 3 is a flow diagram illustrating an example of generating audiencemember profiles. The steps shown in FIG. 3 show the manner in which theextractor 122 obtains profile data indicating the online website pagesvisited by an audience member. In step 210 the extractor searches theoffline databases, such as registration and subscription databases, forprofile data relating to individual audience members. The search of theoffline databases may be initiated by an instruction received from theconsole 140. For example, an instruction could be given to collectprofile data for all audience members who subscribe to the New YorkTimes. Such an instruction necessitates that the extractor 122 haveaccess to the subscription database for the New York Times.

The extraction rules determine the profile data that is collected. Instep 212, the profile data extracted from the offline sources may bestored in the data warehouse. As there may be a need to determine theprofile data that is associated with a particular audience member, theextractor may assign a unique identifier to the profile data in step214. The unique identifier may be a string of numeric, alphabetic,alphanumeric, or other characters that may be used to identify oneaudience member.

In step 216, the unique identifier may be used to identify contentvisited by the audience member. The unique identifier may be so used byincluding it in a domain cookie associated with each website pagevisited by the audience member. Each of these domain cookies may bestored on the computer associated with the audience member, and may beused to identify each particular website page visited by the audiencemember as being associated with the unique identifier. In step 218, theextractor may determine the domain cookies that are stored on theaudience member's computer. Because these domain cookies include theunique identifier that identifies the particular audience member, theextractor may use these cookies to modify the profile data for aparticular audience member to reflect that the audience member visitedthe website pages associated with the cookies. By combining the profiledata obtained from the offline databases with the profile data updatesthat occur as a result of the audience member visiting website pages, acomplete set of profile data may be collected for an audience member,reflecting both offline and online behavior and characteristics for theaudience member.

Tracking the online history of an audience member requires that thesystem be able to uniquely identify audience members. This tracking maybe accomplished by combining a unique identifier for each audiencemember with website pages in the network that the audience member hasvisited.

A method of providing the unique identifier in each of the domaincookies associated with a number of related website pages is illustratedin FIG. 4. Each of the domain cookies associated with the website pagesvisited by the audience member may be modified to include the uniqueidentifier by designating one of the related website page domains as theprimary website domain. A primary domain cookie with the uniqueidentifier is established for the primary website domain. Usually, anetwork will already have a domain that can be used for this purpose. Ifnot, one of the domains in the network may be designated as the primarydomain.

With reference to FIG. 4, an audience member browser 300 initiates theprocess in step 340 by requesting a website page from a site within thenetwork, www.domainl.com 310. Responsive to the website page requestdirected to www.domainl.com 310, a page is returned to the browser 300with an image tag which may reference the targeting engine 152 atte.domainl.com in step 342. In step 344, an image request is sent fromthe browser 300 to the targeting engine 152. If a unique identifier isnot included in the request, in step 346 a redirect is sent to thebrowser 300 to the targeting engine 152 now referenced aste.primarydomain.com. The redirect includes a reference to the originaltargeting engine reference in step 344, te.domainl.com. For example, theredirect may be http://te.primarydomain.com/blank.gif?te.domainl.com. Instep 348, the browser 300 may send this redirect request tote.primarydomain.com. Responsive to this request, in step 350 aprimarydomain.com cookie containing a unique identifier for the audiencemember is assigned to the browser 300. In step 352, a second redirect ismade of the browser 300 to te.domainl.com, that may include the sameunique identifier as set in the primary domain cookie. For example, theredirect may be http://te.domain1.com/blank.gif?tid=7dha6wlk9927sha. Instep 354, the redirect request is returned with the originally requestedimage and a domain1.com cookie with the same unique identifier as theprimarydomain.com cookie.

After the process illustrated in FIG. 4 is completed, an audience membervisit to another website in the network, such as www.domain2.com, mayresult in a request for an image at te.domain2.com. If the TargetingEngine 152 does not detect a domain2.com cookie with a unique identifierfollowing the image request, the Targeting Engine 152 may redirect arequest to primarydomain.com for a cookie. Responsive to this request toprimarydomain.com, the primarydomain.com cookie is read and a redirectis sent back to the browser 300 containing the unique identifiercontained in the primary domain.com cookie. The unique identifier in theprimarydomain.com cookie is the same as previously set. The requestedimage may then be sent to the browser 300 along with the domain2.comcookie which may have the same unique identifier as theprimarydomain.com cookie. This process of providing a domain cookie withthe unique identifier is carried out each time the audience membervisits a new website page for the first time so long as the new websiteis related to the other websites in the network from the viewpoint ofthe Targeting Engine.

The Targeting Engine 152 may be a standalone web server, running onApache, and using a MySQL database on a shared server, although theTargeting Engine 152 may be variously realized using alternativesoftware and separate servers for Apache and the database. The TargetingEngine 152 may direct the setting of an additional cookie that maycontain one or more segment identifiers. These cookies may then be usedby other servers, such as, for example, an ad server, an email server, astreaming media server, and/or a web content server, to deliver targetedcontent to a particular audience member based upon one or more segmentsin the cookie.

With renewed reference to FIG. 2, the audience segmentation and analysisstage may be carried out by the data warehouse 132. The data warehouse132 may assign a particular audience member to one or more segmentsbased upon common profile characteristics. A segment of audience membersmay be defined as a group of audience members to which the system user(such as an advertiser) desires to send the same content. For example,returning to the example discussed above, a segment of audience membersmay be defined as all audience members that the system user selects toreceive a particular golf advertisement. The selection of the audiencemembers for receipt of this advertisement may be based on one or moreaudience member characteristics in the profile data.

A method of associating an audience member with a segment is illustratedin FIG. 5. In step 220, the profile data attribute values of audiencemembers who will qualify for inclusion in the segment may be defined bya set of segment rules. The segment rules may be selected using theconsole 140. Any of number and/or range of profile data attribute valuesmay be used to govern qualification for a segment. In step 222, the datawarehouse 132 may search the profile data to determine the audiencemembers that qualify for the audience segment. This search may becarried out at the request of the system user, and if desired, on aroutine basis, such as daily. In this manner, membership in the audiencesegment may be maintained up to date. In step 224, the data warehouse132 may store segment affinity data to indicate the audience membersthat are included in a particular segment. It is appreciated that thesegment affinity data may indicate that an audience member is in morethan one segment. The segment affinity data is defined by a set of rulesbased upon the behavior and characteristics in the audience profile.Once a set of rules that define the segment affinity data areidentified, a segment identifier is assigned to that particular set ofrules. This segment identifier is then sent to the Targeting Engine 152,along with the audience unique identifier assigned previously by theTargeting Engine 152. In step 226, when the Targeting Engine 152 isnotified that an audience member has requested a website page in thenetwork, the Targeting Engine stores a segment-targeting cookie on theaudience member's computer. The segment-targeting cookie includes thesegment identifier that identifies the segments that the audience memberis included in. The method of storing the segment-targeting cookie on anaudience member computer is described in further detail below inconnection with FIG. 6.

Profile data for audience members may also be manually analyzed to buildsegments. With renewed reference to FIG. 2, the server or servers thathost the Targeting Engine 152 and the data warehouse 132 may beoperatively connected to the console 140. The console 140 may be used todesignate the offline databases used to initially populate the datawarehouse with profile information, to set the rules for collectingprofile information, and to create and view reports showing audiencemember profile data, audience member segment affinity data, and audiencemember Internet activity.

A method of delivering targeted content to an audience member based onthe segment affinity data is illustrated in FIG. 6. With reference toFIG. 6, an audience member requests a website page in the network ofrelated websites in step 230. The Targeting Engine is notified of thewebsite page request in step 232. Responsive to the audience membersrequest for a website page, in step 234 the Targeting Engine determineswhether or not a domain cookie, associated with the requested websitepage, includes a unique identifier for the audience member. If a uniqueidentifier is not identified, the Targeting Engine will provide awebsite domain cookie with a unique identifier as described above inconnection with FIG. 4. Once a website domain cookie is provided with aunique identifier, in step 236 the Targeting Engine may determinewhether or not a segment-targeting cookie is already associated with theaudience member in the data warehouse. The segment-targeting cookie mayinclude a segment identifier that indicates the segment(s) to which theaudience member belongs. If segment affinity data is stored in the datawarehouse for the audience member, then a segment-targeting cookie iscreated and stored in the audience member computer with the appropriatesegment identifier in step 238. In step 240, content may be delivered tothe audience member based on the segment identifier in thesegment-targeting cookie stored in the audience member computer.

If no segment-targeting cookie is identified in step 236, the TargetingEngine may query the data warehouse for any segment affinity dataassociated with the audience member. If no segment affinity data isstored for the audience member, a default segment-targeting cookie maybe stored in the audience member computer. The default segment-targetingcookie may automatically expire after some fixed period of time, such asone day for example.

Once a segment-targeting cookie is stored on the audience membercomputer, the Targeting Engine may periodically update it with newsegment affinity data for the audience member. Updating may occurautomatically at fixed intervals, and/or in response to modifications tothe profile data for the audience member.

A wide variety of content may be provided to the audience member as aresult of the segment-targeting cookie being stored on the audiencemember computer. With renewed reference to FIG. 2, content may include,but is not limited to website page advertisements, pop-upadvertisements, emails, or the like.

The system 10 of the present invention is adapted to segment and targetaudience members for delivering content to an audience member across aplurality of digital mediums. The digital mediums may be heterogeneous,and may include, but are not limited to, a website network, a cablesystem, a non-web based internet network, a wireless communicationssystem, such as a cellular phone or RF network, and/or any digitalmedium in which the means for interfacing the audience member with thedigital content is uniquely addressable. It is contemplated that thedigital medium may include other consumer technologies not yetdeveloped.

FIG. 7 is a block diagram illustrating another example of a system fordelivering content to an audience member. The system includes a digitalcable network 400. The digital cable network 400 may include a hometelevision having a uniquely addressable cable set-top box 480 as ameans for interfacing the audience member with digital content. Thedigital cable network 400 may further include a cable head-end 450 fordelivering segment targeted content to the set-top box 480. As will beapparent to those of ordinary skill in the art, the head-end 450 mayinclude means for receiving a digital signal, such as, for example, asatellite receiving antennae, from a programming processor 460. Theprogramming processor 460 programs the content to be delivered to theaudience member, and provides the appropriate digital signal to thehead-end 450. The programming processor 460 may be in communication witha cable company database 430 which may store, for example, subscriptiondata relating to the audience member. The data may include a uniqueidentifier of the audience member within the cable network 400. Theprogramming processor 460 may interface with the system 10 of thepresent invention through a cable network/Internet bridge 440. Asdiscussed above, the system 10 may include an audience member profile.

The digital cable network 400 may further include a cable companywebsite provided by a web server 470 and accessible by the audiencemember via the Internet. The audience member may access the website 470to request a service, such as, for example, ordering a movie, placing arepair order, and changing the level of cable service. The audiencemember may access the website 470 by providing the audience member'scable network identifier.

The system of FIG. 7 may be operated as follows for delivering contentto an audience member across a plurality of digital mediums. Theaudience member may visit a website provided by a web server 170. Theweb server 170 may receive a request for content from the audiencemember, and provide website pages to an audience member computer 180 ina conventional manner. The website 170 may be owned by, or affiliatedwith, the owner of the cable network 400 and the website 470. Theaudience member may visit other sites related to the website 170 withina network. If necessary, a unique audience member identifier related tothe website network is assigned to the audience member, and profile datais collected and stored, substantially as described above in connectionwith FIGS. 3 and 4. The audience member may be associated with anaudience segment defined by a set of segment rules substantially asdescribed above in connection with FIG. 5.

The audience member may visit the website 470 to request a service fromthe cable company, at the same time providing the audience member'sunique identifier within the cable network 400. The programmingprocessor 460 may read the audience member's web network identifier, andassociate the audience member's cable network identifier with thisidentifier. The programming processor 460 may then access the system 10through the bridge 440, and accesses the segment affinity data relatingto the particular audience member using the web network identifier.Based on the audience segment affinity data, the programming processor460 defines the programming rules for the audience segment within thecable network 400. The appropriate digital signal is then sent to thecable head-end 450, and the head-end 450 delivers the audience membertargeted content via the set-top box 480 and the audience member's hometelevision. The preferences and behavior of the audience member withinthe network 400 may also be used to update the member's profile withinthe system 10. In this manner, the audience member's preference andbehavioral data is synchronized across a plurality of mediums into acommon profile, and the content delivered to the audience member viathose mediums may be customized based upon the characteristics of theprofile.

FIG. 8 is a block diagram illustrating an embodiment of an audiencetargeting system 800 that includes a targeting engine (TE) 810,extractor (Extractor) 820, segment manager (SM) 830, and data warehouse850.

The audience targeting system 800 and its components are illustratedcollectively for ease of discussion. As described previously, thevarious components and corresponding functionality may be providedindividually and separately if desired, such as by different serversthat are assigned to the functionality of one or more of the components.

The functionality of the audience targeting system 800 is preferablyprovided by software that may be executed on any conventional processingsystem, such as those previously named or others. In that regard, theaudience targeting system 800 may in turn be a component of a computersystem containing a processor and memory. Although one modular breakdownis shown, it should be understood that the described functionality maybe provided by greater, fewer and/or differently named components.Although a software embodiment is described, the audience targetingsystem 800 may also be provided as hardware or firmware, or anycombination of software, hardware, and/or firmware.

As previously described, audience segments may be variously calculated,such as on a periodic basis. One model for accommodating audiencesegment calculation is a batch processing model. For example, at 24 hourintervals the Audience Targeting System 800 may prompt a recalculationof all necessary audience segments based upon previously extracted dataas well as any newly extracted data that had been discovered since theprevious batch process. While this model is useful for many applicationsand for certain types of extractable data (e.g., data from registrationsources, surveys and 3^(rd) party data), it is not always the best modelto implement. One issue with the batch processing model is that it canbecome computationally expensive, particularly where audience segmentsare recalculated based upon not only previously extracted data, but thenewly extracted data. Another issue is that certain data sources maycontain data that should be acted on more frequently than dictated bythe batch processing interval. The example of the Audience TargetingSystem 800 illustrated in FIG. 8 accommodates what is referred to as acontinuous processing model, although the batch processing alternativeis also applicable to embodiments of the present invention.

The regular mining and sending of data to the Extractor 820 may besupported by what is referred to as “dock and shuttle” data extractiondescribed further in connection with FIG. 9 below. The segment manager830 and corresponding architecture is described further in connectionwith FIGS. 10A-B below. Recalculation of audience segments based uponincremental data, and processing data tables to manage and produceaudience segments are described further in connection with FIGS. 11A-B.Each of these features may be provided in conjunction with the audiencematching network aspects of the present invention that are described inconnection with FIGS. 14A-B through 17 below.

Still referring to FIG. 8, the Audience Targeting System 800accommodates the collection and coordination of data across multiplesites, as well as the targeting of audience members. In that regard, auser that wants to target a particular audience defines audiencesegments of interest. The audience segments correlate to user profiledata that may comprise both characteristic and behavioral data. Thecharacteristic data is often found in registration data and includesattributes such as age, gender, ZIP code, and household income. On theother hand, behaviors may include attributes such as which sections wereviewed on a site (e.g., sports, entertainment, health), whichadvertisements were seen (e.g., mortgage rates, allergy medication),referrers (e.g., AOL, Yahoo), the time of visiting the site (point intime, or range), and the frequency of visits to the site. Audiencesegments may be defined based upon such user profile data. In turn, theaudience segments form the basis for the information that is extractedfor analysis, reporting and targeting audience members in relevantsegments.

Audience targeting is not limited to web applications. For exampleprofile data might include behavioral attributes such as programsviewed, time viewed, etc., and characteristic attributes such assubscriber IDs or the like in applications involving a television settop box.

The TE 810 provides the means for assigning and coordinating uniqueidentifiers corresponding to individual audience members. As previouslydescribed, when an audience member logs onto a page for the first time,the TE 810 places a cookie on their browser, which contains a uniqueidentifier. Whenever that audience member returns to the site, theunique identifier is sent back to the TE 810. Based upon the uniqueidentifier, the Audience Targeting System can set a segment cookie,which can be used for the delivery of targeted content such as ads,e-mails, etc. to the audience members computer or other relevant device.The TE 810 may also create logs of this activity. The unique identifiermay be referred to as a profile identifier (PRID).

Another example of an extractor 900 is further described with referenceto FIGS. 9A-B, which respectively are a block diagram illustrating anembodiment of an extractor 900 and a schematic diagram that exemplifiesa model for extracting profile data. Although particular terms such asdock and shuttle are used because they are helpful in conceptuallyillustrating this aspect, it is noted that various alternativeterminology may be used for elements that perform the same functions.

The Extractor 900 includes a shuttle 902, dock 904 and extraction module906. The functionality of the so-configured Extractor 900 is bestunderstood with concurrent reference to FIG. 9B, which also refers toother elements. The shuttle 902 may be code that resides on the datasource. Its purpose is to mine local data locally and send it to theextractor (more specifically, the dock 904 on the extractor). In oneembodiment, the shuttle 902 accomplishes this by assembling boxes. Thedock 904 receives boxes and, when sufficient boxes are available (or atimeout occurs) creates a pallet 908 out of the boxes. The extractorworks on those pallets 908. In order to accomplish this reliably, it isuseful for the shuttle 902 to know where the source data resides. Forthe previously mentioned batch processing embodiments, it is also usefulfor the shuttle to handle the situation where log files “roll” and arearchived by the customer. In this regard, the shuttle 902 interfaceswith log data such as that provided by conventional log file generatingelements (e.g., Apache).

A data agent may also be employed to assist in the gathering ofinformation from website visitors. This may be provided in the form ofcode that is added to those pages in connection with which datacollection is sought. The code may have header and function callportions that respectively identify the functions and variables that itneeds to operate and ensure that all variables have been collected. Thedata agent may be configured to produce log lines suitable for receiptand processing by the TE. Examples of parameters include the version ofthe data agent, the page referrer, the page URL, time information, andthe PRID. As will be described below in connection with profilesynchronization, a REGID parameter may be provided as well. In additionto association with PRID as described, a cookie may delineate a uniqueREGID for an audience member in the same fashion. Another “cookie list”(CLIST) parameter may be used to identify the list of cookies thatshould be captured.

The dock 904 is the receiving area on the Extractor that manages theordering and processing of pallets. Data from the shuttle 902 may begrouped into what is referred to as boxes. Generally, a box contains asingle event, but in some cases (e.g. OAS logs) a single record maycontain several events. An event may be a time tagged user action on asource server. Examples of events may include a web page view, an adimpression, etc. A pallet 908 may be a collection of boxes, and istypically a collection of data mined from the data source and packagedfor delivery to the extractor dock 906.

Various data sources may be supported by this model, but in oneembodiment web log data is the data source. The shuttle 902 may be apersistent C++ application that processes data from a log file or pipe.Upon startup, the shuttle 902 finds the current log file (or pipe) andopens it for reading. In addition, the shuttle 902 establishes aconnection to the dock 904 in order to be able to deliver pallets 908 tothe extraction module 906 for processing.

The shuttle 902 may be configured to process data in a persistent loopuntil an unrecoverable error or external termination signal occurs.During the processing loop, the shuttle 902 reads up to a configurablenumber of available items (log lines) from the source and packages theminto a box. If there are more items available than the maximum number ofitems, or if the total size of the items are greater than the maximumbox size, the extra lines are written into an overflow buffer and willbe inserted first into the next box created.

Once the box has been created, the shuttle 902 sends the box to the dock904, along with an indication of the size of the box for validationpurposes. The extraction module 906 acknowledges and validates the boxand responds with an acceptance signal before the shuttle 902 will dropthe existing box and repeat the processing loop.

More than one shuttle 902 can connect to a given dock to allow formultiple machines which all serve the same data source (e.g., multipleweb servers responding to a single domain via a load balancer). Datafrom different shuttles 902 in a given dock is sorted into bays. Thesebays contain the unprocessed data for a given data source from a givenshuttle.

The extraction module 908 is preferably configured to handle each datasource type, and may include sub-modules for each different data sourcetype (e.g., one for each of OAS, W3C, IIS, etc.).

Finally, the extraction module 906 is responsible for processing data aspallets from the dock 904 and creating the output that gets sent to thedata warehouse 850 for final import processing. Basically, theextraction module 908 component performs extraction as described inconnection with the previously described embodiment of the Extractor(from FIGS. 1-7). The processed data may be referred to as profile data.In one embodiment, the profile data may be organized and thus providedas fact tables that are described further below.

The segment management aspect is now further described with reference toFIG. 8, which illustrates the segment manager 830 to include a segmentorganization module 832 that includes a console management module 834, asegment generation module 836 that includes a new segment calculationmodule 838 and a segment recalculation module 840, and a reportingmodule 842.

The segment manager 830 accommodates the definition and management ofsegments corresponding to audience members based upon characteristic andbehavioral information. The segments are organized according to ahierarchical logical tree based architecture that allows scalablesegment management and accommodates incremental recalculation ofsegments.

The segment organization module 832 facilitates user-definition ofaudience segments according to this architecture. It operates inconjunction with the console manager 834 which provide interfaces thatallow users to define and configure segments according to the samelogical architecture. These interfaces may be in the form of panels thatillustrate segments and combinations of segments to produce new segmentswhich will be further understood upon explanation of the architecturebelow.

The segment generation module 836 generates segments comprisingappropriate audience members based upon the so-defined audiencesegments. The new segment calculation module 838 calculates newsegments, and the segment recalculation module 840 calculates existingsegments, in particular taking incremental data and recalculating suchsegments, thus avoiding the need to fully calculate the segment asthough it were new each time new data arrives.

The segment generation module 836 may be configured to process segmentscontinuously (e.g., as a Windows service). For each pass, the segmentgeneration module 836 reads a table in the database warehouse 850 thatcatalogs segments, to determine which segments it should process on thatpass. A type identifier associated with the segments may indicatewhether the segments are to be calculated anew, and thus passed to thesegment calculation module 838, or incremental, and thus passed to thesegment recalculation module 840.

Finally, the reporting module 842 communicates with the segmentorganization 832 and segment generation module 836 and producescustomizable reports. The designer is free to structure the reportingoptions as desired. One example of a report is a “Known AudienceInside/Outside” report, which reports on the behavior of an audiencesegment in the sections outside the section behavior that defines thesegment. For example, An Inside/Outside report on viewers of the Newssection would show the audience members behavior inside news and comparethat to all other sections of the site. This may be used to targetvaluable behavior on other parts of the site. Another example of areport is a “Reach and Frequency Report”, which reports on the reach(total audience) and frequency (number of times seen) for one or more adcampaigns. The reporting module 842 may implement conventional reportingtools including but not limited to Crystal Reports as provided byBusiness Objects SA, San Jose Calif.

FIGS. 10A-B are schematic diagrams illustrating an example of a segmentmanagement architecture 1000(a-b) and corresponding calculation ofsegments according to another aspect of the present invention. Asintroduced above, the profile data includes attributes that arecorrelated to audience members, and is the basis of the audience segmentdefinitions that are used to target audience members with advertisementsand/or other content.

Profile data may also be organized as “facts” that have one or moreattributes. For example an “Age” fact may have one attribute—Age.However, an “ID” fact may have several attributes such as the PRID or aregistration identifier (REGID) that uniquely identifies registration atthe site. A “Section” fact may contain attributes for the Section, toplevel Section (that is, if Section is/News/InternationalPolitics, TopLevel Section would be/News), second level section(/News/International), site (site that section belongs to) and full path(Site+Section).

Profile data and the individual attributes comprising the profile datamay be categorized as being (1) Characteristics (e.g., Age, Gender,Household Income); (2) Behaviors (e.g., Page Views, Ad Clicks); (3)PRID; or (4) Business Unit ID, which describes the site that a behavioroccurred on.

The attributes may also be said to have dimensions or values that may bedefined in tables for ease of computation. Moreover, attributes may befurther defined based upon whether they are single or multi-valued. Forexample, Age, Gender, HHI are characteristics for which an audiencemember will only have a single value (e.g., an audience member cannot beboth Male and Female). Conversely, behaviors have multiple values peraudience member and some characteristics (e.g., e-mail newsletterssubscriptions) also have multiple values.

The hierarchical architecture facilitates efficient calculation of themembership of audience segments. Lists of audience members belonging toparticular segments may be maintained. These membership lists may belogically combined to determine the membership of dependent (e.g.,child) audience segments.

As indicated, the segment management architecture 1000 a includes aseries of attribute segments, namely Section 1002, Gender 1004, andHousehold Income (HHI) 1006 as provided in this example. Base segmentshave attributes with particular values that correlate to relevantattribute segments 1002-6. Base segments for any number of attributescould be provided (e.g., different behaviors different sections;different gender, different HHI). The illustrated segments are “VisitedNews” 1010, “Male” 1012, and “HHI>$100K” 1014. Each of these may beconsidered as separate and distinct segments. However, these segmentsmay also be logically combined to create new segments that depend fromthem. For example, the segment “Males who have Visited News” 1020comprises a logical combination of the Males 1012 and Visited News Last1010 segments. Still further, a third level in the hierarchy of segmentsmay be defined as “Males who have Visited News with HHI>$100K” 1030,which comprises a logical combination of the previously describedsegment 1020 with base segment 1014 (HHI>$100K). In this fashion, thesystem may variously organize segments, and this same organization canbe used as the basis for guiding the user through the definition ofsegments via the console manager 834. Notably, there may be instanceswhere a user defines a complex segment directly, wherein the systemautomatically generates the base and any intervening segmentsaccordingly, to facilitate calculation and recalculation of segments.

For ease of illustration, a logical “AND” operation has been described,which basically provides the intersection of two parent segments. Thesegment manager 830 supports various additional logical operations orset expressions, including “EXISTS”, which inserts entries from oneparent; “OR”, which inserts entries from the union of two parents; aswell as “exclusive AND”, and “exclusive OR”. Attribute expressions mayalso be used, such as one which inserts entries from a given parentsegment that match specified criteria.

In addition to providing improved organization of segments, the segmentmanagement architecture 1000 a facilitates proper maintenance of asegment population where incremental profile data is processed, withoutrequiring a full calculation of the segment. That is, introduction ofthe new information to the existing segment is accommodated throughlimited processing involving the new information, in lieu of calculatingthe segment based upon application of its definition to the cumulativeset of data. To accommodate this, entry and exit rules are implemented.An “entry” corresponds to an introduction of audience members to aparticular segment based upon the incremental data, and an “exit”corresponds to a removal of audience members from a segment. Entries arebasically audience members found to currently meet the criteria, butwhom are not yet associated with the previously calculated segment.Exits are the opposite—they are audience members found to no longer meetthe criteria.

FIG. 10B illustrates an entry and exit 1032 functionality for thesegment management architecture 1000 b. As described above, theExtractor continuously populates the data warehouse with profile datathat identifies various attributes. As indicated, a Gender' attributesegment 1004′ is generated responsive to incremental profile data. Thisgenerally represents audience members that have attributes defined underthe attribute segment “Gender” within the incremental profile data.Among those are the previously described “Male” segment 1012. In thatregard, exit and entry membership lists are built. Specifically, allaudience members identified as being male in the incremental profiledata are provided in an entry membership list for the Male segment 1012.Similarly, all those audience members who do not have the relevantattribute (which may be referred to as “not male”) are provided in anexit membership list for the Male segment 1012. Exit and entry rules arethen used to determine how to accommodate an appropriate update to thesegment. The entry may be accommodated by taking the union of theexisting membership in Male 1012 with the membership list in the entrymembership list for Male. The exit may be accommodated by removing fromthe existing membership in Male 1012 those audience members listed inthe exit membership list (actual removal, of course, would only beapplicable for those present prior to the recalculation).

For ease of discussion, focus is made on incremental profile data as itrelates to Gender, but the principle of exit and entry can apply to anysegment including but not limited to Visited News, HHI and others.

Incremental profile data based recalculation also propagates through thehierarchy. This may be variously arranged, again depending upon exit andentry rules, which in turn depends upon the logical relationships of thesegments. For a dependent (child) segment resulting from an ANDoperation such as Males who Visited News 1020, this may compriserepeating application of the above-described entry and exit membershiplists for “Male” to the segment Males who Visited News 1020 in a similarfashion. That is, the entry membership list for Males would be added tothe Males who Visited News 1020 segment, and the exit membership listremoved. Alternatively, base segments Male 1012 and Visited News 1010could be recalculated with their respective entry and exit membershiplists, and then Males who Visited News 1030 could be calculated basedupon the intersection of the updated versions of Male 1012 and VisitedNews 1010.

If desired, recalculation of a dependent segment could also be basedupon a calculation based upon the updated parent segments. Specifically,the entry and exit 1032 functionality could be applied to the basesegments, which could then be used to

FIG. 11A is a schematic diagram illustrating an example of processing1100 data tables to manage and calculate segments according to anotheraspect of the present invention. The illustrated processing correlateswith the segments that are defined in the example of FIGS. 10A-B. Asdescribed, the Extractor operates to collect information about numerousaudience members and provides such information in the data warehouse.That information may be organized so that attributes corresponding toindividual audience members may be identified. The illustrated facttables 1102 a-d are a preferred technique for organizing the informationas such. In one embodiment, each fact in a fact table is associated withan audience member using their unique identifier (PRID). A fact tablecontains all facts related to all users for a particular attribute.Accordingly, there is a section fact table that contains all sectionfacts, an age fact table, a gender fact table, etc. Each row in a tablerepresents a piece of data (characteristic or behavior) associated withonly one audience member (more specifically one PRID).

As described attributes may involve characteristics such as age andgender as well as behaviors such as the number of times that theaudience member has visited a particular section (News, Sports, etc.).At times, an attribute may be determined by looking at multiple piecesof information. Thus, while gender may be a simple determination ofwhether gender=“male”, an attribute that includes frequency informationsuch as how many times an audience member visited a particular sectionmay involve counting the number of entries in a fact table for theaudience member. This counting may also be constrained to those entriesfalling within a particular time period.

Various alternatives may be used to provide the functionality of thefact tables, including different organization of the information. Forexample, the system may alternatively construct a table that provides alisting of attributes for a user identified by a unique PRID. his wouldresult in a number of fact tables respectively corresponding to uniqueaudience members identified by their PRIDs.

As previously described, the Segment Manager accesses the informationstored in the data warehouse and maintains segment definitions, such asthose input by the user seeking certain audience segments. A givensegment is calculated by determining which audience members have theattribute for the given segment. According to this aspect of the presentinvention, the association of audience member identifiers to attributesand hierarchical logical tree based segment architecture accommodatevery efficient calculation (and recalculation) of segments.

A first level of processing 1104 may be used to calculate base segments.This is done by identifying the attribute for a base segment and thendetermining the audience members (or more particularly the listing ofPRIDs) that have that attribute. Presume that segment 1.1 is the“Visited News” segment (see FIG. 10A). In this instance, the SegmentManager examines the fact tables and collect the PRIDs for those facttables that contain this attribute. As indicated in segment table 1106a, this may result in a determination that PRIDs 1, 2, 4, 6, and 7 havethe given attribute. The listing of PRIDs in a segment table may also bereferred to as the “membership list” for the given attribute/segment.Again, there may be millions of members in a segment, the limitedlistings are used for ease of illustration.

The segments may also be identified by identifiers (SEGIDs) in lieu ofthe words and phrases that identify them. Thus associating identifiersSEGID_(x.x) with the noted PRIDs efficiently identifies the audiencemembers with the attribute for computational purposes. Each segment maybe organized in this fashion.

Continuing with the example, segment 1.2 may correlate to the attribute“Male”. Audience member PRID₁ is identified as male, and is listed inthe segment table for segment 1.2, but PRID₂, identified as female, isnot. The table 1106 c for segment 1.3 (HHI>$100K) includes both of thosePRIDs. Again, segment tables for each of the segments may be provided,for x base level segments (1106 a-d).

A next level of segments may then be calculated 1108 from the basesegments. This aspect of the present invention accommodates efficientdetermination of further levels of segments through application ofvarious Boolean operations to the existing segment tables. For example,Segment 2.1 may have been defined as “Visited News” AND “Male”. This isaccommodated by determining the intersection of the PRIDs in those twosegment tables (1106 a, 1106 b). As illustrated, the segment table 1110a for segment 2.1 thus includes PRID₁, PRID₄, and PRID₆ since thoseidentifiers appeared in both of the two base segment tables. Table 1110a thus lists audience member identifiers for the males who have visitedNews. Once again, any number of segments may be calculated 1108 at thislevel, denoted as tables for segments 2.1 through 2.y (1110 a-b).

Still further calculation 1110 accommodates determination of the nextlevel of segments. Segment 3.1 (“Males who have visited News withHHI>$100K”) correlates to a combination of Segment 2.1 (Males who havevisited News) and Segment 1.3 (HHI>$100K). Again, the logical ANDimplements the intersection of the relevant segment tables, whichresults in listing PRID₁ and PRID₄ as belonging to segment 3.1, persegment table 1114 a. Any number of z segments may be calculated 1112(segment tables 1114 a-b).

The segment tables are the membership lists for their respectivesegments, and may be updated accordingly responsive to segmentrecalculation upon receipt of incremental profile data as previouslydescribed. FIG. 11B illustrates how the segment tables are updatedresponsive to recalculation based upon receipt of incremental data.Here, entry and exit is accommodated by tables containing membershiplists, or entry tables and exit tables. As previously describedincremental profile data (denoted respectively as fact tables 1102a′-d′) is received, and entry and exit tables are built based upon suchdata. FIG. 11B illustrates how the information in the entry and exittables is useful for recalculating segments. Suppose that the entrytable for the “Males” Segment 1.2 includes PRID₇ and the exit table forthe same segment includes PRID₄. Application of the exit table wouldprompt PRID₄ to be removed from “Males” Segment 1.2 (as denoted bycross-hatching). Application of the entry table would cause PRID₇ to beadded to the segment (as denoted “entry”). The membership of dependentsegments is also updated according to the previously described logic.That is, because PRID₄ is no longer a member of Males Segment 1.2, it isalso removed from dependent segment Males who have Visited News 2.1.Continuing to the next level of dependency PRID₄ is removed from Segment3.1, but PRID₇ is not added because Segment 3.1 is an AND combination ofSegments 2.1 and 1.3, and PRID₇ is absent from Segment 1.3.

Note that different logical combinations will prompt differentapplication of entry and exit upon recalculation. Segment 2.1 is alogical AND of Segments 1.1 and 1.2; if it were a logical OR combinationof those segments, then PRID₄ would not be removed unless it was alsoremoved from Segment 1.1.

Another aspect of the present invention provides profilesynchronization. People may access various computers throughout the dayand week, such as a home computer, office computer, mall kiosk, or thelike.

As described above, PRIDs are unique identifiers that are used toidentify and gather data regarding unique audience members. In thatregard, when a new visitor (e.g., a woman using her office computer) toa web site is encountered, they are associated with the next availablePRID (e.g., PRID_(A)). Cookies implemented in conjunction with thevisitor's browser then include the particular PRID_(A) and are used tocollect profile data for that visitor. Later on, the same person may useher home computer to visit the web site. Presuming that the homecomputer has not been used to access the site, there will not berecognition that she is the same person, and a new unique PRID(PRID_(B)) will be generated and associated with her behavior andcharacteristics from that computer. There will thus be two separate setsof profile data that actually correspond, unbeknownst to the AudienceTargeting System, to the same person.

Further, the person may use another computer (e.g., mall kiosk) thataccesses the web site, and yet another unique PRID_(C) may be issued.This is problematic in two ways. First, it creates a third separate PRIDfor activity corresponding to the same person. Also, the mall kiosk (oreven home and office computers) may be used by multiple people. Eventhough multiple different people are using the computer and engaging invarious behavior, it will all be tracked as PRID_(C).

Still another problem is potential deletion of cookies. Continuing withthis example, if this audience member deletes cookies on her officecomputer, then correlation with PRID_(A) is lost and she will beperceived as a new visitor on the next web site visit, promptingissuance of PRID_(D) in association with her office computer. This isproblematic because the segments associated with PRID_(D) will notreflect information previously gathered in connection with PRID_(A).Also, PRID_(A) will essentially become a defunct PRID, but will still bewastefully processed by the system.

FIG. 12 is a block diagram illustrating an example of an audiencetargeting system 1200 that includes profile synchronization 1260according to another aspect of the present invention. Profilesynchronization variously corrects and mitigates problems associatedwith these conditions. In one embodiment, the PRID is a system basedidentifier that uniquely identifies an audience member. An authoritativeidentifier (e.g., a registration identifier) is also sought andmaintained in association with a profiled audience member. Anauthoritative identifier may be identified in connection with somecollected profile data. Maintenance of associations betweenauthoritative identifiers and PRIDs allows such collected profile datato be properly associated with a particular audience member despite theabsence of a PRID in the collected data. This functionality alsoaccommodates the potential generation of multiple cookie basedidentifiers by a particular audience member. In contrast to the systemidentifier (PRID), ⋅ which may also be referred to as an internalidentifier, these cookie based identifiers are examples of externalidentifiers (XIDs). Maintenance of associations between each profiledaudience member's PRID with one or more XIDs allows management ofmultiple external (e.g., cookie based) identifiers in association with aparticular audience member.

Before turning to a more detailed discussion of profile synchronization,it is noted that in embodiments of audience targeting that do notimplement profile synchronization, the XID may essentially equate withthe PRID for the purpose of audience member profile management. It isalso noted that although cookie based XIDs are described, other externalidentifiers such as those that correlate to usage of a non-web devicemay also be implemented.

The Audience Targeting System 1200 includes a TE 1210, Extractor 1220,Segment Manager 1230 and Data Warehouse 1250. These elements areanalogous to the commonly named elements in the previously describedAudience Targeting System (800, FIG. 8) and need not be repeated withregard to the profile synchronization aspect.

As with the previously described system, the Audience Targeting System1200 and its components are illustrated collectively, but may beprovided individually and separately if desired. The functionality ofthe Profile Synchronization module 1260 is preferably provided bysoftware that may be executed on any conventional processing system. Inthat regard, the audience targeting system 1200 (or any sub-module) mayin turn be a component of a computer system containing a processor andmemory. Although one modular breakdown is shown, it should be understoodthat the described functionality may be provided by greater, fewerand/or differently named components. Although a software embodiment isdescribed, the functionality may also be provided as hardware orfirmware, or any combination of software, hardware, and/or firmware.

The Profile Synchronization module 1260 includes an ID Management module1262, an Authoritative ID Recognition module 1264, and an ID Storagemodule 1266 that in turn stores profile identifiers (PRIDs) 1268, REGIDs1270, and XIDs 1272.

Profile synchronization entails a recognition that audience members, andthe potential multiple identifiers that they may become associated with,may be associated with an authoritative identifier (ID). TheAuthoritative ID is in turn used to manage the multiple identifiers aswell as the profile data associated with the audience member. In oneembodiment, the Authoritative ID is associated to registration (e.g.,login credentials, REGID) for the user web site. For example, the website may be The New York Times web site, which might requireregistration and login for usage of certain elements of the site.

The Profile Synchronization module 1260 implements PRIDs to uniquelyidentify audience members even as they generate multiple XIDs. In thatregard, PRIDs may be regarded as system level, or more particularlyAudience Targeting System 1200 level unique identifiers, and XIDs asaudience member machine level based unique identifiers.

To accommodate the profile synchronization functionality, the ID Storagemodule 1266 stores the various ID information, including PRIDs 1268,REGIDs 1270, and XIDs 1272. The ID Management module 1262 organizes theissuance of and relationships between the various ID information. Itaccommodates this by associating the PRID for a particular user asuniquely identifying them on the system. This information may be storedalong with other characteristics information such as the first date thatthe audience member was recognized by the system. Tables and the likemay also be used to associate the audience member's PRID to the XIDsthat are correlated to that audience member using profilesynchronization, as well as to the REGID to accommodate recognition ofaudience members in conjunction with the Authoritative ID Recognitionmodule 1264, which determines the presence of authoritativeidentification and communicates with the ID management module 1262 toensure proper issuance of corresponding unique IDs.

The functionality of the Profile Synchronization module 1260 is furtherdescribed with reference to the flow diagram of FIG. 13, whichillustrates an example of a process 1300 for profile synchronization.

In support of the profile synchronization functionality, a new uniqueXID is associated 1302 with a first time visitor to the web site. Ifregistration is applicable for the session, then the REGID is associatedas well. These functions are provided during regular browsing of pagesand facilitated by the data agent as described above. Also in thedescribed fashion, the data warehouse is populated with profile datacorresponding to audience members. Unique REGIDs are thus alsoassociated to respective sets of profile data along with the uniqueXIDs.

The profile data may be retrieved 1304 from the data warehouse in thepreviously described fashion. In embodiments using fact tables, thismeans that entries identifying both the XID and the REGID will beprovided in association with the listed attributes. The fact tableincludes at least an XID, denoted particularly as XlD_(P) in thisexample. A first determination 1306 is made as to whether a REGID isalso included in the fact table. As described, the REGID is used as theauthoritative ID. In its absence, the system seeks to process the databy attempting to associate the fact table with a PRID. As described, alist of XIDs is maintained in association with each PRID. Thisinformation is examined to see whether the particular XID (denotedXID_(P)) is found. If found, it is mapped to at least one PRID. It maybe possible that an XID is mapped to multiple PRIDs. In that case thesystem may choose a random PRID, the first one found, or use anyalgorithm to select one. It should be noted that fact tables may bevariously organized to provide this functionality. In one example ofthis the different attributes (Section, Age, Gender, Referrer, etc.) mayeach have a different table where a particular value is associated to aparticular profile via the PRID.

With profile synchronization, the PRID uniquely identifies audiencemembers for the purpose of segmenting. Accordingly, when it isdetermined 1308 that a particular PRID is associated with the particularXID_(P), segments are calculated 1310 associating the attributes in thefact table to that particular PRID. If a PRID is not determined 1318 tobe associated with XID_(P), then a new PRID_(Q) is issued 1312. Inconjunction with that, XID_(P) is mapped to PRID_(Q), and segments arecalculated accordingly.

If it is determined 1306 that a REGID is present in the fact table, suchis construed as the authoritative ID. This may be the first instancethat the system sees a particular REGID, in which case a PRID isassigned (denoted PRID_(R)) and mapped to the REGID (1316).

If it is determined 1314 that there is already a PRID associated withthe particular REGID (i.e., not the first instance of seeing REGID),then the particular PRID (the unique PRID number for that audiencemember) is associated to the fact table attributes and correspondingsegments. Additionally, if such is not already the case, XID_(P) isincluded 1318 in the list of XID numbers that the system has associatedto the particular PRID.

If desired, the segment manager may also segregate segments for anaudience member using the XID list. For example, a particular audiencemember may have two XIDs associated to their unique PRID. One XID maycorrespond to his home computer and another XID may correspond to hiswork computer. Although the system will (through connection to theauthoritative ID as described above) conclude that he is the same personand that all of the activities from both computers could be commonlysegmented under the unique PRID, the listing of XIDs in association withthat PRID allows the system to generate separate segments if desired.This may in fact be desirable to certain users of the Audience TargetingSystem since in some instances an audience member may have separate homeand office personas in terms of computer usage and desired ad exposure.

FIGS. 14A-B are schematic diagrams illustrating an example of anAudience Matching Network 1420 according to an aspect of the presentinvention. The previously described audience targeting systemsaccommodate the definition of audience segments, the collection ofprofile data and corresponding determination of membership in thosesegments, and the delivery of content to audience members falling withinsuch segments. A number of audience targeting systems (ATS) 1440, 1442,1444 may respectively correspond to domains in which the process ofdefining segments and delivering content is carried out. Particularly,ATS 1440 may correspond to “a.com”, ATS 1442 to “b.com” and ATS 1444 to“c.com.” That is, visitors to a.com are audience members that may betargeted with advertising as they navigate among web pages in the a.comdomain, via the functionality of the audience targeting system (here,ATS 1440) as previously described in detail.

According to one aspect, the present invention provides a network formatching an audience with deliverable content, which may be referred toas an Audience Matching Network (AMN) 1420. The AMN 1420 is anaudience-centric network that allows advertisers to use behavioraltargeting in combination with demographic data to reach defined audiencesegments of significant size. Preferably, the demographic data will benon-personal data. In some embodiments, personal data may be used, asconstrained by audience member consent, contractual, and/or legalrequirements.

The profile data for audience members may be collected across numeroussites having the audience targeting functionality (e.g., ATS 1440-1444).Each of these sites offers potential segments and population of suchsegments with members in their respective domains. These domains mayalso be referred to as “local” domains. These domains may, for example,each correspond to a separate publisher. As is well known, publishersmay display online advertisements on pages, and advertisers typicallypay publishers to place these advertisements.

The AMN 1420 has a domain (e.g., audiencematchingnetwork.net, oramn.net) that is common to the entire network of sites, which may bereferred to as the network domain. The AMN 1420 presides over networklevel segments that comprise the various segments respectively populatedby the ATS 1440-1444. As such, the AMN 1420 is able to organize andmanage segments based upon attributes that collectively traverse thenetwork, and that otherwise would not have been identifiable byindividual sites.

With continued reference to FIG. 14A, individual ATS may define segmentsand collect profile data for audience members in the relevant domain.For example, ATS 1440 may uniquely profile a.com audience member “123”,under a unique identifier denoted as PRID₁₂₃.

In the illustrated example, a “Traveler” segment may correlate toaudience members who have been determined to be interested in travel,such as by their having visited a travel-related page in the domain.This particular segment in the a.com domain is denoted and managed assegment #100. The audience member managed under PRID₁₂₃ is determined tobe a member of segment #100 (“Traveler”).

It should be noted that this example is illustrative only. As describedabove in connection with the ATS and segment management features ofrelated inventions, segments may be variously defined according tocharacteristic and behavioral attributes, including but not limited togender, section visited, HHI and others. It is also noted that datacollection may not be limited to segment data, but may be any datapoints that a publisher allows the AMN 1420 to collect.

Still referring to FIG. 14A, in the b.com domain, ATS 1442 defines a“business” segment #200 and through collection of profile data withinthe domain, determines that the audience member uniquely identified inb.com as PRID₄₅₆ is a member of that segment. Further, in the c.comdomain, ATS 1444 defines another segment #300 and determined that itsuniquely identified audience member PRID₇₈₉ is a member of that segment.

Each of these audience members may be targeted for the delivery ofcontent within respective domains. According to this aspect of thepresent invention, the AMN 1420 is able to recognize that an audiencemember that is (separately) uniquely profiled in different domains is infact the same audience member, and to assemble segments thatcollectively traverse the numerous domains in the network, so as todetermine that such an audience member is a member of a complex segmentbased upon information collected in the local domains.

The AMN 1420 uses a unique identifier referred to as a network PRID(NPRID) to manage the unique identification of audience members at thenetwork level and to determine segment membership. The AMN 1420 alsomanages network level segments. For example, a “Business Traveler”segment may be managed as segment #5000 by the AMN 1420, and may bemapped to the segments (#100, #200) respectively defined in one or moreof the domains in the network. The segment “Business Traveler” #5000 isshown for ease of discussion and to illustrate how the AMN 1420 maydefine “complex” segments that result from a combination of segmentsdefined in separate local domains in the network.

The “Business Traveler” segment may be variously calculated, butpreferably may comprise individual network level segments “Business” and“Travel” that are hierarchically organized. The determination ofmembership within base level segments and higher level segments thatcombine such base level segments may be accommodated through suchorganization of the segments. Also, membership tables can be used tocalculate and recalculate segment membership using the NPRID identifiersat the network level.

As an alternative to having different segment identifiers for local andnetwork domains, it may be more efficient to have a segment definitionscheme that is universal to the network and local domains. Such a schemewould use “global” segment identifiers. Thus, for example, both thea.com ATS and the AMN 1420 may define segment #100 as “Travel”. Thisavoids mapping segments. As another alternative, the local domains maycollect behavioral and characteristic information for passage to thenetwork domain. The network domain would then collectively have thebehavioral and characteristics information upon which targeted deliveryof content may be based. The network designer is free to establish thesystem as desired.

The NPRID may be assigned the first time that an audience member isestablished with any one of the individual domains in the network, andcookie information may be used by the AMN 1420 to recognize a profiledaudience member for future visits, even if such visits are to sites inother domains in the network. Specifically, presume that a particularaudience member visits a.com, and that the visit is the first of anysite in the network. In connection with this, the particular audiencemember may be assigned PRID₁₂₃ by ATS 1440. In conjunction with this,the ATS 1440 (or, more particularly, the data agent associated with ATS1440) directs the particular audience members browser to the AMN 1420,which assigns a unique NRPID (e.g., NRID₁₀₁₁₁₂) to the particularaudience member. In connection with this, the particular audience memberbrowser may be provided with cookie information that identifies theparticular audience member as PRID₁₂₃ in a.com and NPRID₁₀₁₁₁₂ inamn.net. Subsequently, the audience member may visit b.com, and beassigned PRID₄₅₆ by ATS 1442. However, when the browser is directed toAMN 1420, the AMN 1420 recognizes the audience member NPRID₁₀₁₁₁₂ basedupon the cookie information and does not assign a new number. The AMN1420 may, however, retain a listing of PRIDs corresponding to an NPRID.The cookie information may be variously organized. One example uses alocal cookie corresponding to the local domain and a network cookiecorresponding to the network domain.

The NPRID, in turn, is used to manage network segment membership andother network profile data values. Thus, for example, NPRID₁₀₁₁₁₂ wouldappear in the membership listing (e.g., tables) for the network levelsegments “Business” and “Travel”. A combination of these segments wouldprovide a “Business Traveler” segment that would have NPRID₁₀₁₁₁₂ as amember. Also in connection with the collection of data and determinationof segment membership, the AMN 1420 includes a targeting engine that,like the targeting engine in the ATS, may set segment cookies thatidentify the segments to which a particular audience member belongsbased upon any collected network profile data. In this instance, suchsegment cookies are set in the amn.net domain.

FIG. 14A illustrates, among other things, the data collection role formembers of the audience matching network. Another role that is providedin connection with the AMN 1420 is an “Ad Serving” role, illustrated inFIG. 14B. Although data collection may be performed by formal partnersthat are part of the audience matching network, the serving of ads toaudience members who have visited sites in the audience matching networkis not necessarily limited to those formal partners. “Non-Partner Site”d.com 1460 illustrates an example of an ad serving site, which servesaudience matching network ads but does not collect data. Of course,partner sites may also fill the Ad Serving role.

In addition to allowing non-partner sites to serve AMN ads, the AMN 1420accommodates the indication whether an audience member currently has anyvalues in the network segment cookie that may be targeted against. Theindication may be in the form of an AMN cookie (AMNC) with a Y/N value,where a “Y” indicates that there are values and an “N” indicatesotherwise.

The serving of ads may be as follows. Someone visits 1480 the AMN AdServing Site 1460 (in the “d.com” domain). The AMN Ad Serving Site willlikely incorporate an ad server (the ad server for d.com) to provideadvertisements in connection with pages requested by the visitor, and assuch the visitor's browser is redirected 1482 to the d.com ad server(1484). An initial determination is made whether AMNC is set to “Y” inconnection with the visitor's browser. If this is not the case (or theAMNC Y/N is completely absent), then the d.com ad server servesnon-AMN-network ads to the visitor's browser in convention fashion.However, if the AMNC is set to “Y”, then the browser is redirected 1486to the ad server in the amn.net domain (1488). There, the visitor isrecognized as, say, NPRID₁₀₁₁₁₂ and is served 1490 ads appropriate forthe segments in which NPRID₁₀₁₁₁₂ is a member.

FIG. 15 is a block diagram illustrating an embodiment of an AMN System1500 that includes a targeting engine (TE) 1510, Extractor 1520, SegmentManager 1530, Audience Member Management 1540, Content Delivery andManagement 1550, and Data Warehouse 1560 modules.

The AMN System 1500 and its components are illustrated collectively forease of discussion. The various components and correspondingfunctionality may be provided individually and separately if desired,such as by different servers or agents that are assigned to thefunctionality of one or more of the components.

The functionality of the AMN system 1500 is preferably provided bysoftware that may be executed on any conventional processing system,such as those previously named or others. In that regard, the AMN system1500, or individual elements thereof, may in turn be a component of acomputer system containing a processor and memory. Although one modularbreakdown is shown, it should be understood that the describedfunctionality may be provided by greater, fewer and/or differently namedcomponents. Although a software embodiment is described, the AMN system1500 may also be provided as hardware or firmware, or any combination ofsoftware, hardware, and/or firmware.

As with the previously described analogous component in the ATS, the TE1510 accommodates the assignment and coordination of unique identifierscorresponding to individual audience members in conjunction with thecollection of data and the setting of cookies to support such collectionof data, and, ultimately, the delivery of targeted content to theaudience members.

With the AMN System 1500, when an audience member logs onto a page forthe first time in the domain, the TE 1510 places a cookie on theirbrowser, which contains a unique identifier. In contrast to the TE forthe ATS, the domain is the audience matching network, which comprisesthe local domains of the various partner sites. Accordingly, the uniqueidentifier for the network is set the first time any page for anypartner site is visited.

This network level unique identifier is referred to as an NPRID, asdescribed above. Although individual local sites may manage a profilethrough the previously described PRID, the AMN System 1500 uniquelyidentifies an audience member network-wide through the NPRID. Whenever aparticular audience member with a previously assigned NPRID returns toany partner site (e.g., a.com, b.com, or c.com in FIGS. 14A-B), theunique NPRID is sent back to the TE 1510. Using the NPRID as a basis toidentify the audience member, the AMN System 1510 can set a networklevel segment cookie, which is subsequently used for the delivery oftargeted content to the audience member's device. The TE 1510 may alsocreate logs of this activity, useful for performance and revenuedeterminations.

The issuance of NPRIDs may be provided in conjunction with an AudienceMember Management module 1540, which includes an network profile module(NPM) 1542 and a participation verification module 1544. The NPM 1542manages the issuance of NPRIDs and retains lists of PRIDs correspondingto each unique NPRID. It may also communicate with the Segment Manager1530 and thereby retain the lists of segments to which an audiencemember defined by an NPRID belongs.

The participation verification module 1544 accommodates the managementof the AMNC value, including setting of the AMNC and changes to the AMNCsetting. The AMNC accommodates an indication whether the correspondingaudience member has values that may be targeted against.

The Extractor 1520 for the AMN System 1500 is similar to that for theATS (FIG. 8, element 820), functioning in the network domain rather thanthe local domain.

Particularly, the AMN System 1500 Extractor 1520 similarly uses a dataagent that is employed to assist in the gathering of information fromwebsite visitors, again provided in the form of code that is added tothose pages in connection with which data collection is sought. Thatdata agent may be thought of as having a network component and severallocal components, corresponding to the local domains. The primarydifference is that the local data agent component sends data within thelocal domain, whereas the network data agent sends data within thenetwork domain. As with the previously described data agent, the codemay have header and function call portions that respectively identifythe functions and variables that it needs to operate and ensure that allvariables have been collected. The data agent may also be configured toproduce log lines suitable for receipt and processing by the TE.Examples of parameters include the version of the data agent, the pagereferrer, the page URL, time information, and the NPRID. Furthermore,the data agent may be used to carry out the setting of cookies relatedto the described AMNC value for determining participation in the AMN.

The Segment Manager 1530 is preferably as previously described inconnection with the ATS Segment Manager (830, FIG. 8), and thussimilarly includes segment organization, console management, segmentgeneration with calculation & recalculation, and reporting modules,which need not be re-described. Notably, the Segment Manager 1530accommodates the definition and management of segments corresponding toaudience members based upon characteristic and behavioral information.The segments may be organized according to a hierarchical logical treebased architecture that allows scalable segment management andaccommodates incremental recalculation of segments. Membership lists maybe used to determine which audience members are part of which segments,with calculation and recalculation of segments upon exit and entry beingprocessed in the same fashion. The significant distinction between theATS segment manager and the AMN Segment Manager 1530 is that, in lieu ofusing the PRID as the basis for determining membership in segments, theSegment Manager 1530 uses the described NPRID. As described above, theSegment Manager 1530 may map ATS segments to network segments.Alternatively, ATS in the local domains may merely providecharacteristic and behavioral information to the Segment Manager 1530,which collects the information and manages the definition and populationof segments in any fashion that is desired.

The Content Delivery and Management module (CDMM) 1550 allows thedetermination of which advertisements (or other content) are to bedelivered to which network-level audience segments. In the context ofthis aspect of the present invention, this may merely be anidentification of which advertisement corresponds to which segment. Thisfunctionality, and the corresponding information, may be exported to theAMN Ad Server for efficient serving of ads to visitors of sites in thenetwork.

In addition to identifying the association of advertisements tosegments, the CDMM 1550 may manage a bidding process whereby advertisersbid on AMN audience segments. The CDMM 1550 may also apply revenue andperformance based management of audience segments and correspondingaccounting. These aspects are described further in connection with FIGS.17 et al. below.

Finally, the data warehouse 1560 is populated with and stores thevarious profile data as previously described, but does so at the networklevel.

FIGS. 16A, 16A-1, 16A-2, 16A-3, 16B, 16B-1, 16B-2, and 16B-3 are eventdiagrams illustrating an example of a computer implemented process 1600for matching audience members to deliverable content according to thepresent invention. The described components of the AMN System aresegregated to give a further understanding of the process 1600. For thea.com and b.com domains, the site and ad server are illustrated, as arethe segment manager and targeting engine components of the ATS. For theamn.net domain, the AMN ad server, AMN targeting engine and AMN segmentmanager are shown.

The process initiates with a visitor (aka an audience member) requesting1602 a page from Site A. In this instance, the audience member has beenpreviously engaged and assigned PRID₁₂₃, and the requested page isrelated to travel, or is among those pages deemed to indicate an currentinterest in travel. Accordingly, the data agent for the domain causes acommunication to be sent 1604 to Segment Manager A, indicating that theaudience member with PRID₁₂₃ has requested the relevant page. This isjust an example of various behavioral and characteristic informationthat could be provided to a segment manager. In response, SegmentManager A processes 1606 received profile data, resulting in theinclusion of audience member PRID₁₂₃ in the travel segment, denoted assegment #100.

The segment information is passed 1608 to the Targeting Engine A. Thevisitor's browser is also prompted to call 1610 the Targeting Engine A,which receives the PRID, determines 1612 which segments correspond tothat PRID, and then sets 1614 a local cookie to include a reference tothe segment. Here, PRID₁₂₃ is associated with segment #100 in the a.comdomain, so Targeting Engine A acts accordingly. Data agent code on apage visited by an audience member may initiate the call 1610. In thiscontext, it would be any page subsequent to the page that generated theaforementioned data.

The AMN data agent also prompts the visitor's browser to provide 1616information to the AMN Targeting Engine. For this example, the audiencemember is assumed to have previously been identified by the AMN Systemand as such has already been assigned NPRID₁₀₁₁₁₂. ⋅ The inclusion ofthis audience member in a.com segment #100 is among the providedinformation, which is passed 1618 to the AMN Segment Manager, which thenmaps 1620 the a.com segment #100 to the AMN segment for Traveler,denoted as segment #4000. As described previously mapping is optional,and other forms of local collection and network organization ofbehavioral and characteristics information that do not require mappingor even local segment definitions may be used.

Finally, the AMN Segment Manager reports 1624 back to the AMN TargetingEngine, specifically that NPRID₁₀₁₁₁₂ a member of network segment #4000.The AMN Targeting Engine then sets 1626 two cookies. The local (a.com)cookie is set to include identification that AMNC=Y, and the (AMN)network cookie is set to indicate that the audience member NPRID₁₀₁₁₁₂ amember of network segment #4000 (and any other network segments to whichthe audience member belongs).

In connection with visiting site A, the audience member browser is alsodirected 1628 to the a. com domain Ad Server A to for advertisements(these may be those populating the currently visited page). Inconnection with this, Ad Server A is configured to inquire 1630 whetherAMNC is set to indicate that an AMN network ad should be served, withthe “Y” value indicating such to be the case. Presuming that AMNC doesindicate this, the Ad Server A redirects 1632 the visitor's browser tothe AMN Ad Server. (If AMNC=N, the Ad Server A would simply serve alocal advertisement).

The previously described setting of the network cookie includes theidentification of the audience member as NPRID₁₀₁₁₁₂ as well asmembership in segment #4000, and this information is thus sent 1634 tothe AMN Ad Server in connection with obtaining the network ad. The AMNAd Server processes 1636 this information, which results in sending 1638the advertisement associated to the traveler segment to the particularaudience member.

Continuing with the description of the process 1600 in connection withFIG. 16B, the audience member may similarly visit the b.com domain. Thesequence of recognizing the audience member in connection with behaviorthat suggests “business” interest is particularly described. As was thecase with the visit to the a.com domain, here the audience memberrequests 1640 a page identified as noting an interest in business. Theaudience member is (again, previously) identified as PRID₄₅₆ by theb.com ATS and as such the local cookie notes this information. The b.comdomain data agent causes a communication to be sent 1644 to SegmentManager B, indicating that this audience member PRID₄₅₆ has requestedthe business page, and the Segment Manager B processes 1646 theinformation accordingly, resulting in the inclusion of audience memberPRID₄₅₆ in the business segment, denoted as segment #200.

The Segment Manager B passes 1648 the information to the TargetingEngine B. Again, the visitor's browser is also prompted to call 1650 theTargeting Engine B, which receives the PRID, determines 1652 whichsegments correspond to that PRID, and then sets 1654 the local cookie toinclude a reference to the segment (#200).

As with the previously described visit to a.com, the AMN data agentprompts the visitor's browser to provide 1656 information to the AMNTargeting Engine. The audience member has previously been identified bythe AMN System and assigned NPRID₁₀₁₁₁₂. The inclusion of this audiencemember in b.com segment #200 is among the provided information, which ispassed 1658 to the AMN Segment Manager. If necessary, the AMN SegmentManager maps 1660 the b.com segment #200 to the AMN segment forBusiness.

The AMN Segment Manager may also determine membership in segments thatresult from a combination of local segments. Here, membership in“Business Traveler” is determined based upon membership in “Traveler”from data collected in the a.com domain, and membership in “Business”from data collected in the b.com domain. This “Business Traveler”segment may also be organized and managed numerically, such as networksegment #5000 as noted. Also in connection with the segment, adetermination 1662 is made whether the segment is targetable.

The AMN Segment Manager similarly reports 1664 to the AMN TargetingEngine that NPRID₁₀₁₁₁₂ is a member of network segment #5000. The AMNTargeting Engine then sets 1666 the local and network cookies, with thelocal (b.com) cookie set to include identification that AMNC=Y, and the(AMN) network cookie set to indicate that the audience member NPRID₁₀₁₁₂is a member of network segment #5000.

The audience member browser is also directed 1668 to the b.com domain AdServer B for advertisements. Ad Server B is configured to determine 1670whether AMNC is set to indicate that an AMN network ad should be served,and if so redirects 1672 the visitor's browser to the AMN Ad Server. Theidentification of the audience member as NPRID₁₀₁₁₁₂ as well asmembership in segment #5000, is sent 1674 to the AMN Ad Server inconnection with obtaining the network ad. The AMN Ad Server processes1676 this information, which results in sending 1678 the advertisementassociated to the business traveler segment to the particular audiencemember.

According to another aspect, the AMN System facilitates performancebased content delivery, as well as accounting for revenue correspondingto the delivery of advertising. FIG. 17 is a block diagram illustratingan example of an AMN system 1700 that includes advertising revenue andperformance management.

As with the previously described AMN system (1500, FIG. 15), the AMNSystem 1700 includes a targeting engine (TE) 1710, Extractor 1720,Segment Manager 1730, Audience Member Management 1740, Content Deliveryand Management 1750, and Data Warehouse 1770 modules.

Except as noted below, these modules 1710-1770 are similar to theanalogous modules in the previously described AMN System. Also, the AMNSystem 1700 and its components are again illustrated collectively forease of discussion. The various components and correspondingfunctionality may be provided individually and separately if desired,such as by different servers or agents that are assigned to thefunctionality of one or more of the components. As with the previouslydescribed AMN system, the functionality of this AMN system 1700 ispreferably provided by software that may be executed on any conventionalprocessing system, but may be variously provided and/or modularized asdesired.

The most significant different between this AMN System 1700 and thatpreviously described is the inclusion of placement 1752, bid process1754, and revenue and performance modules 1756 in the CDMM 1750. Theplacement module 1752 provides the basic capability for accommodatingthe placement of content, typically in conjunction with an AMN adserver, as previously described.

The bid process module 1754 accommodates the bidding on network segmentsby advertisers. Advertisers bid on available segments, to help ensurethat their advertisements target audience members corresponding to adesired segment, when those audience members subsequently visit thesites in the AMN network, or those sites that partner with the AMN toplace advertisements. The AMN Ad Server will preferably be prompted toselect an ad with the highest bid for a segment that has not already itsperformance goals, has not exceeded its run dates and is not excluded bya publisher exclusion rule. An audience member may be part of more thanone segment, so this process occurs across multiple segment targets. Theprocess may also provide a ranking of advertisements. Pages ofteninclude multiple locations for advertisements, and both the ranking ofadvertisements for a given segment and the membership in more than onesegment may contribute to which advertisements are placed on a page. Thealgorithm for placing advertisements may be variously altered asdesired. For example, a randomized entry of non-highest bidadvertisements may be used to allow them to be placed, which may resultin determining that such an advertisement is a leading performer in agiven segment.

The revenue and performance module 1756 includes a placement performancemodule 1758, an information influence determination (IID) module 1760, arevenue sharing module 1762, and a dynamic segmentation module 1764.

Before describing the individual elements of the revenue and performancemodule 1756 in more detail, reference is made to FIG. 18, which is aflow chart providing an overview of delivering advertisements withrevenue and performance management by the AMN System. As described indetail previously, information is collected 1802 corresponding toaudience members who are visitors to web sites that comprise a networkof information collection domains. Based upon the collected information,it is then determined 1804 that an audience member is a member of anetwork segment. This involves a determination that the audience memberprofile data evidences attributes that are defined by the networksegment. Armed with the knowledge that the audience member belongs tothe network segment, the delivery of an advertisement is accommodated1806. As described above, this is based membership of the audiencemember in network segment. It is also, preferably, based uponperformance criteria that are described further in connection with therevenue and performance module 1756 below. In one embodiment, theperformance criteria are configured to benefit the publishercorresponding to the placement of the advertisement. Finally, revenuemay also be allocated 1808 corresponding to the delivery of theadvertisement based upon participation as a data provider in thecollection of information.

Still referring to FIG. 17, the placement performance module 1758generally determines which advertisements to deliver, based upon variouscriteria but preferably making publisher (web site) revenue paramount inplacement determinations. The revenue sharing module 1764 allocates thesharing of revenue related to ad placement and related activitiesaccording to the role that a party provides. The information influencedetermination module 1760 works in conjunction with the revenue sharingmodule 1762 and helps in the allocation of revenue by establishing theinfluence that various different pieces of information is deemed tohave, particularly with regard to how recent such information wascollected. The dynamic segmentation module 1764 accommodates thecreation of segments to maximize the revenue and yield fromadvertisements placed in connection with such segments.

In addition to the delivering advertisements to audience membersbelonging to network-defined segments based upon behavior and thedesired target audience of an advertiser as described above, theplacement performance module 1758 optimizes the delivery ofadvertisements from an existing 3^(rd) party network. For example, apublisher may seek to optimize the delivery of all their inventorycurrently served by a 3^(rd) party ad aggregator or optimization service(such as Google, Overture, Kanoodle, Advertising.com, etc).

In this regard, the AMN network acts as a gateway for publisherpartners, proxying requests for ads on the publisher's pages, with theplacement performance module 1758 choosing the correct ad for a givenpage/position based upon a set of criteria including maximizing revenueand fulfilling inventory commitments.

Two examples of maximizing revenue to a publisher include 1) choosing,from among the pool of possible ads, those ads for which the publisheris paid the most money (impression model) and 2) increasing thelikelihood of generating a click-through for any given ad by providingadditional behavioral targeting criteria not currently available to the3^(rd) party networks.

In the first example, the publisher provides the AMN System with itsimpression goals and the CPM (cost per thousand) for each of thepotential ad deliveries/networks. The placement performance module 1758then uses this information to direct the AMN Ad Server to choose, basedupon current delivery metrics (e.g., number of impressions previouslydelivered against a given campaign, CPM), the advertisement that willprovide the most revenue to the publisher. This arrangement improves onthe approach of simply serving the ad with the highest CPM in everyinstance, as each campaign has a maximum impression goal beyond whichthe advertiser or network will not pay for impressions.

In the second example, the AMN Ad Server has available the segmentaffiliation of the visitor and can choose, based upon the targetcriteria of the campaigns, the ads most likely to generate a click fromthe visitor, thereby maximizing the revenue for the so calledpay-per-click campaigns. Click likelihood in this case may be determinedin a number of ways. One example is to evaluate the click history of theaudience member and see what ads s/he has clicked on in the past.Another example would be to look at the ads that have the highest clickrate with respect to the target segment or other segments. There mayalso be a feedback loop, of clicks/per impression/price per click todetermine the value of an ad, in lieu of purely determining the ads mostlikely to be clicked.

These are just two examples of optimization that may be performed by theAMN System through the placement performance module 1758. Publishers andadvertisers may provide various types of information for application toad delivery opportunities as desired.

The IID module 1760 determines the influence that information has on anaudience member's behavior. According to this aspect of the presentinvention, not all data is presumed to have equal value in influencingthe behavior of an audience member. For example, some data may be morevaluable in influencing behavior of one type (e.g., auto purchase) whileother data is more valuable for other types of behavior (e.g.,traveler). The IID module 1760 evaluates the relative impact thatdifferent pieces of data have upon the effectiveness of a particularsegment. These effectiveness metrics can then be used for determiningthe priority of an ad to be served to an audience member (i.e. tomaximize the revenue by serving the ads most likely to be clicked on bythe audience member) and for the determination of distribution ofrevenue to data providers as described with regard to the RevenueSharing module 1762, below.

Various factors that may go into determining the impact that individualdata points have on the overall effectiveness of a segment, and may beconfigured as desired by the system designer. Examples of factorsinclude recency, frequency, correlation analysis across segments withsimilar data values, data half-life analysis (described below), primacy(first site to provide data value), and intensity (which is similar tofrequency but is measured as frequency per unit time).

The data half-life analysis refers to a process whereby an analysis isdone of the overall effectiveness of data values over time to determineat what point a data value's effectiveness has been reduced by half. Forexample, it is well known that car buying behavior is relevant for lessthan 90 days as a consumer researches and then either purchases orabandons the purchase process. In one embodiment, this is determinedstatistically by looking at the click-through rates on ads targeted atsegments including particular data values and correlating thisinformation with the age of the data provided. This is used to generatea curve where the effectiveness declines over time. This half-lifeanalysis is then used to influence the value of a particular data valuewhen evaluating a segment that contains that data value.

In addition, data provider metrics may also influence the value that aparticular data point has in the overall effectiveness evaluation. Forexample, cars.com or Edmunds might be considered a more reliableindicator of auto buyer interest than similar content at USAToday.com orthe Dallas Morning News website.

Finally, a further improvement on equal weighting of all data values isconsidering the value of the audience that is a part of a given segmentin determining the effectiveness of the data in a segment. This isprovided by analyzing behavior by a segment. If the audience includedhas a high proportion of audience members that have shown a willingnessto click on ads, this information can be used to further enhance theeffectiveness profile of the segment. Other metrics in this categoryinclude analysis of the average number of segments that the audiencemembers in a given segment are a part of This could show a more valuableaudience because the diversity of data about this audience, leading tomore “touch points” and hence, opportunities to present relevantadvertisements.

The Revenue Sharing module 1762 accommodates allocation of revenue amongrelevant participants in the delivery of advertisements. Generally, theRevenue Sharing module 1762 builds upon standard practices in theindustry where by two or more partners agree to split the revenuegenerated by an ad placement.

In one case, this involves inclusion of the publisher that provides thedata about a targeted audience member and may thereby be referenced as a“data provider” in the revenue sharing arrangement. The data provider iscompensated based upon the fraction of the data used in the targeteddelivery provided by the data provider. For example, if an audiencemember is classified into the network segment “Business Travelers”, andthe data is provided in line with the example described above, thena.com and b.com would split the data provider revenue when a BusinessTraveler clicks on an ad, since they each contributed ½ the datainvolved in the segment that resulted in a click. In addition, theaudience provider (publisher partner that served the ad that was clickedon) will also receive a percentage of the revenue.

The Revenue Sharing module 1762 also applies data value and audiencevalue metrics, as described regarding the IID module 1760, to therevenue sharing arrangement. This accommodates rewarding the dataproviders that contribute the audience member or data value that hadmore impact on the successful conversion of the audience member (that isto say, the click event) with a higher percentage of the data providerportion of the revenue.

Finally, the Revenue Sharing module 1762 may also determine dataprovider value metrics in line with the information provided by the IIDmodule 1760. In this scenario, the brand or nature of a data provider'ssite will result in a higher or lower apportionment of the data providerrevenue share based upon the value of the brands market awareness orauthority of the data providers content.

The dynamic segmentation module 1764 generates segments based upon acorrelation analysis. This may informally be referred to as a “peoplelike this” approach. This allows an advertiser to identify a desiredaction or series of actions and tag the pages (on their site or others)that represent the desired action (purchase, visit of a promotionalsite, etc). Based upon the historical behavior of the visitors that takethe desired action, the dynamic segmentation module 1764 correlates theprofiles of visitors that have the desired behavior with all otherprofiles to find those visitors that have not taken the desired actionbut show a close affinity to those profiles that have taken the desiredaction. Consequently, the AMN Ad Server may be instructed to target apromotional advertisement to those visitors that have not yet taken theaction, with a high degree of likelihood that these users will also takethe desired action.

FIG. 19 is a block diagram illustrating an example of an AMN system 1900that includes universal profile synchronization (UPS) according toanother aspect of the present invention. The AMN System 1900 includes atargeting engine 1910, Extractor 1919, Segment Manager 1930, CDMM 1950,and data warehouse 1960. Each of these modules are described inconnection with the AMN system (1500) of FIG. 15 and need not bere-described in connection with this aspect of the present invention. Incontrast to that system, the AMN System 1900 audience member managementmodule 1940 is updated to include a Universal Profile Synchronization(UPS) module 1946.

Cookie blocking technologies have become an increasing problem foronline publishers. While there are certainly many legitimate uses forthis technology, the overly broad approach that most implementationstake affects not only the troublesome adware and spyware that most userswish to block, but also the benign state management cookies that mostonline publishers rely on to manage their business and theirrelationship to the consumer.

As described above in connection with profile synchronization in thecontext of an ATS, publishers typically retain information that can beused as an authoritative identifier for a given audience member, forexample registration login credentials. The publisher also has aninterest in protecting the privacy of audience members, and to enforcethe obligations in its privacy policy. The AMN System, configured toinclude the UPS module 1946, implements authoritative identifiers alongwith the collective information of the network to provide a robustnetwork profile that can help overcome the problems associated withcookie blocking technologies. That is, an audience member can bereconnected with their data after cookies may have been deleted—or evenif the audience member moves to a different client machine.

This dramatically simplifies the consumer profile management process forboth publisher and consumer by creating a single authoritative sourcefor site-specific data, as well as a clearing house for global,non-personally identifiable consumer data that may be shared across someor all publishers within the network.

The UPS functionality implements two primary requirements- thecollection of specific data points associated with an anonymous profileat the publisher site and a network mechanism for identifying thatvisitor authoritatively upon visiting any site within the network.

For each publisher in the network, this entails code within the contextof the publishers domain(s) that captures the identified data pointswhen a visitor is consuming content on that site. The AMN system dataagent, as previously described, captures the local segment affiliationfrom the segment cookie in the content domain and sends that informationback to the AMN Targeting Engine. With the UPS functionality, the dataagent is further augmented to know what data points each publisher hasauthorized for capture and where to find that data. The publisherscontent delivery engine—via HTML tags within the content pages—mayexplicitly pass this data to the data agent. The data agent configuredas such is referred to as the UPS data agent (UPSDA).

Once the UPSDA has captured the data, it sends the data back to the UPSData Collection Engine (UPSDCE). The UPSDCE is similar to the describedTargeting Engine (for both the ATS and AMN Systems). However, the UPSDCEimmediately captures the data points and stores them in a local databaseassociated with the audience member's profile.

Preferably, the UPSDCE resides within a domain owned and managed by theprovider of the audience matching network, in order to allow for theassociation of data to a global profile (NPRID), rather than just thelocal profile (PRID) of the individual publisher. The data also clearlyidentifies which publisher the data collected belongs to, in order toensure that audience members' privacy is maintained by not inadvertentlyco-mingling different publishers data. The assets of each publisher arealso protected by not sharing this data with other publishers, unlessspecific agreements exist to share data within the network.

To properly maintain data integrity, it is useful to authoritativelyidentify a visitor within the network. There are a number of differentways to accomplish this. One approach is to utilize those publishersthat have required registration and login to re-identify the visitor.Another approach is to use client side browser plug-in technology tostore an authoritative identifier for that visitor. Either or both ofthese techniques may be used to assist in authoritative identificationof a visitor.

Once a visitor is identified, the login credentials can be used to lookup the visitor in the network repository (that is, the data setcollected previously by the UPSDCE) and compare the current set ofcookie values with those in the repository. If no cookies are currentlyset, or if the data values do not match, the visitor's cookies areupdated to reflect the profile cookie data.

A visitor to the network need only be authoritatively identified once inorder to authoritatively identify this visitor to all sites within thenetwork, since the network identification can serve as a key into theprofile data for that visitor across all sites.

A further service is the ability to share limited anonymous profile dataacross the network, based upon the publisher's willingness to opt-incertain information and also in compliance with the publisher andnetwork privacy policies.

Finally, it is important that this data get updated on each page that avisitor views in order to ensure current and correct values, many ofwhich are likely modified during a visitors session with the site.

FIG. 20 is a flow diagram illustrating an example of a process for UPSas implemented by the appropriately configured AMN System. The process2000 generally arises in connection with the maintenance and profiles ofaudience members targeted for the delivery of content. As described,this involves receiving 2002 profile data for a plurality of audiencemembers corresponding to a network of local domains which may bereferred to as audience member information collection domains. A givenaudience member has an associated set of profile data based uponinformation collected in the various local domains. The NPRID is used tomaintain 2004 an association between the set of profile data and theprofiled audience member. According to this aspect of the presentinvention, the NPRID is related 2006 to a set of information thatfacilitates the collection of profile data for the profiled audiencemember in the network of information collection domains. Thisinformation is preferably cookie related information that is used inconnection with the collection of data in the network and various localdomains, referred to previously as network cookie and local cookies. TheNPRID is also associated with the previously identified authoritativeidentification for the profiled audience member.

In connection with a visit to any site within the network, anauthoritative identification is received 2008. This information may bereceived in the absence of the NPRID. The authoritative identificationidentifies the profiled audience member in connection with activity, andis used 2010 to correlate the profiled audience member to the NPRID. Inturn, the NPRID is associated to the cookie related information asdescribed. This allows a comparison 2012 of the cookie informationconnected with the current activity with that stored in association withthe NPRID. Such information can be used to update 2014 the cookieinformation in association with the audience member's browser, even ifthe cookies have been deleted between past profiling and the currentbrowsing activity, or even if the audience member uses a differentmachine (if desired). Such updating may of course entail restoring thecookie information previously established for this particular-audiencemember.

Thus embodiments of the present invention provide an audience matchingnetwork and related aspects. Although the present invention has beendescribed in considerable detail with reference to certain embodimentsthereof, the invention may be variously embodied without departing fromthe spirit or scope of the invention. Therefore, the following claimsshould not be limited to the description of the embodiments containedherein in any way.

1-22. (canceled)
 23. A computer-implemented method for delivery ofcontent to a client computer, the method comprising: defining aplurality of segments relating to a plurality of information collectiondomains; determining that a client computer is a member of a segment ofthe plurality of segments by determining that profile data relating tothe client computer includes attributes matching a logical combinationof one or more facts for the segment; receiving, from a server, asegment identifier for the segment of which the client computer isdetermined to be a member; transmitting to the client computer asegment-targeting cookie that includes the segment identifier; andtransmitting an advertisement to the client computer based on thesegment identifier in the segment-targeting cookie.
 24. The method ofclaim 23, further comprising: selecting an advertisement fortransmission to the client computer with a highest available payment toa publisher.
 25. The method of claim 24, further comprising: receiving,from the publisher, a set of delivery criteria corresponding topotentially deliverable advertisements, wherein selecting theadvertisement for transmission to the client computer with the highestavailable payment to the publisher is based upon a comparison of currentdelivery metrics to the set of delivery criteria.
 26. The method ofclaim 23, further comprising: optimizing transmitting the advertisementbased on advertisement inventory served by a third party.
 27. The methodof claim 26, wherein optimizing transmitting the advertisement based onadvertisement inventory served by the third party includes providinginformation about membership in the segment in connection with anadvertisement serving function of the third party.
 28. The method ofclaim 23, wherein the segment is defined by examining the profile datafor client computers.
 29. A computer-implemented method for allocatingrevenue between a plurality of data providers, the revenue correspondingto delivery of content to a client computer having access to a networkdomain and a plurality of information collection domains, the methodcomprising: defining a plurality of segments relating to the pluralityof information collection domains; determining that the client computeris a member of a segment of the plurality of segments by determiningthat profile data relating to the client computer includes attributesmatching a logical combination of one or more facts for the segment;receiving, from a server, a segment identifier for the segment of whichthe client computer is determined to be a member; transmitting to theclient computer a segment-targeting cookie that includes the segmentidentifier; accommodating the transmission of an advertisement to theclient computer based upon the segment identifier in thesegment-targeting cookie; and allocating revenue corresponding to thedelivery of the advertisement between the plurality of data providersbased upon participation of the plurality of data providers as sourcesof the profile data.
 30. The method of claim 29, wherein the informationcollection domains include a first domain and a second domain that isseparate from the first domain, and wherein the set of attributesinclude a first attribute based upon profile data collected inassociation with activity by the client computer in the first domain anda second attribute based upon profile data collected in association withactivity by the client computer in the second domain.
 31. The method ofclaim 30, wherein allocating revenue includes accounting forparticipation of a first of the plurality of data providers forproviding profile data upon which the first attribute is based and asecond of the plurality of data providers for providing profile dataupon which the second attribute is based.
 32. The method of claim 29,further comprising: determining a value that the collected profile dataused to determine that the client computer is a member of a segment isdeemed to have on audience member behavior; and accounting for the valuein allocating revenue corresponding to the delivery of theadvertisement.
 33. The method of claim 32, wherein determining the valueincludes an examination of a set of factors associated with thecollection of the information.
 34. The method of claim 33, wherein theset of factors includes at least one factor selected from the groupcomprising recency, frequency, and primacy.
 35. The method of claim 33,wherein the set of factors includes a half-life analysis.
 36. The methodof claim 33, wherein the set of factors includes at least one factorselected from the group comprising recency, frequency, segmentcorrelation, half-life analysis, primacy, and intensity.
 37. A systemfor delivery of content to a client computer, the system comprising: amemory having processor-readable instructions stored therein; and aprocessor configured to access the memory and execute theprocessor-readable instructions, which when executed by the processorconfigures the processor to perform a plurality of functions, includingfunctions to: define a plurality of segments relating to a plurality ofinformation collection domains; determine that a client computer is amember of a segment of the plurality of segments by determining thatprofile data relating to the client computer includes attributesmatching a logical combination of one or more facts for the segment;receive, from a server, a segment identifier for the segment of whichthe client computer is determined to be a member; transmit to the clientcomputer a segment-targeting cookie that includes the segmentidentifier; and transmit an advertisement to the client computer basedon the segment identifier in the segment-targeting cookie.
 38. Thesystem of claim 37, wherein the processor configured to access thememory and execute the processor-readable instructions further includesfunctions to: select an advertisement for transmission to the clientcomputer with a highest available payment to a publisher.
 39. The systemof claim 38, wherein the processor configured to access the memory andexecute the processor-readable instructions further includes functionsto: receive, from the publisher, a set of delivery criteriacorresponding to potentially deliverable advertisements, whereinselecting the advertisement for transmission to the client computer withthe highest available payment to the publisher is based upon acomparison of current delivery metrics to the set of delivery criteria.40. The system of claim 37, wherein the processor configured to accessthe memory and execute the processor-readable instructions furtherincludes functions to: optimize transmitting the advertisement based onadvertisement inventory served by a third party.
 41. The system of claim40, wherein optimizing transmitting the advertisement based onadvertisement inventory served by the third party includes providinginformation about membership in the segment in connection with anadvertisement serving function of the third party.
 42. The system ofclaim 37, wherein the segment is defined by examining the profile datafor client computers.